Understanding pain-related facial behaviors is essential for digital healthcare in terms of effective monitoring, assisted diagnostics, and treatment planning, particularly for patients unable to communicate verbally. Existing data-driven methods of detecting pain from facial expressions are limited due to interpretability and severity quantification. To this end, we propose GraphAU-Pain, leveraging a graph-based framework to model facial Action Units (AUs) and their interrelationships for pain intensity estimation. AUs are represented as graph nodes, with co-occurrence relationships as edges, enabling a more expressive depiction of pain-related facial behaviors. By utilizing a relational graph neural network, our framework offers improved interpretability and significant performance gains. Experiments conducted on the publicly available UNBC dataset demonstrate the effectiveness of the GraphAU-Pain, achieving an F1-score of 66.21% and accuracy of 87.61% in pain intensity estimation.
@inproceedings{wang2025graphaupaingraphbasedactionunit,title={GraphAU-Pain: Graph-based Action Unit Representation for Pain Intensity Estimation},author={Wang, Zhiyu and Liu, Yang and Gunes, Hatice},year={2025},booktitle={IJCAI Micro-gesture Analysis for Hidden Emotion Understanding (MiGA) Workshop},address={Guangzhou, China},eprint={2505.19802},archiveprefix={arXiv},primaryclass={cs.LG},}
